Machine learning system for assessing heart valves and surrounding cardiovascular tracts

ABSTRACT

A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Provisional PatentApplication No. 61/894,814, entitled “Machine Learning System forAssessing Heart Valves and Surrounding Cardiovascular Tracts,” filed onOct. 23, 2014. The full disclosure of the above-listed patentapplication is hereby incorporated by reference herein.

FIELD

The present disclosure relates generally to the fields of machinelearning, computer modeling and simulation, and computer aided design.More specifically, the disclosure relates to computer-based machinelearning systems and methods for constructing and executing models ofcardiac anatomy and physiology. These models may be used fortherapeutic, treatment, and/or diagnostic purposes.

BACKGROUND

Cardiovascular disease is the leading cause of death in the UnitedStates and claims the lives of more than 600,000 Americans each year.According to the American Heart Association (AHA), more than fivemillion Americans are diagnosed with heart valve disease each year, anddiseases of the aortic and mitral valves are the most prevalent.Combined, aortic and mitral valve diseases affect more than five percentof the U.S. population.

The proper assessment and diagnosis of heart valve operation and thecondition of surrounding cardiovascular tracts are essential forensuring high quality patient care. To this end, several imagingmodalities may be used to inspect the condition and function of heartvalves and the surrounding vasculature. Transthoracic andtransesogophogeal echocardiography, for example, use ultrasoundtechnology to create two- and/or three-dimensional images of heartvalves and the surrounding inflow/outflow tracts (e.g., left ventricularoutflow tract, ascending aorta). Further, computed tomography (CT) andmagnetic resonance imaging (MRI) may also be used.

All imaging modalities have strengths and weaknesses that may limittheir ability to provide a complete and comprehensive assessment ofanatomic and/or physiologic condition. The spatial resolution ofechocardiographic images, for example, may inhibit a detailed analysisof functional operation, especially for highly calcified heart valves.Computed tomography may provide higher resolution images thanechocardiography, but CT imaging studies are more costly and exposepatients to radiation that is potentially harmful. In addition, contrastagents, which may be highly nephrotoxic and may be associated withalterations in renal function, are often used during CT examinations.Hence, new and novel methods that enable an accurate anatomic andphysiological assessment of heart valves and the surroundingvasculature, while not exposing patients to excessive risks orprohibitive costs, are desirable.

Patients diagnosed with symptomatic and clinically significant heartvalve abnormalities may be candidates for valvular repair orreplacement. When repair or replacement is indicated, an accurate andcomplete understanding of valvular anatomy is essential to ensure afavorable outcome. In addition, the anatomic and physiologiccharacteristics of the inflow and outflow tracts that surround the heartvalve(s) must also be understood.

New methods for assessing the anatomic and/or physiologic condition ofnative and prosthetic heart valves and the surrounding inflow/outflowtracts should enable more accurate and precise treatment planning Thesenew methods may complement and/or work in conjunction with existingmethods, or they may stand alone. Regardless, such technologies mustprovide clear and demonstrable benefits to the physician(s) who treatpatients with heart valve disease and/or diseases of the surroundingcardiac tracts. Further, new technologies must not expose patients toexcessive medical risks and should be cost effective.

Therefore, to improve diagnostic and treatment capabilities, it isdesirable to have a system for quickly and accurately assessing thephysiological function, condition, and morphology of heart valves andthe surrounding inflow/outflow tracts, which thereby enables the properdiagnosis of heart valve disease and, if warranted, facilitatestreatment planning

DESCRIPTION OF RELATED ART

There are many academic and industrial research groups that use computermodeling and simulation to analyze flow through heart valves.Historically, valvular hemodynamic analyses have focused on the aorticheart valve and have employed methods of computational fluid dynamics(CFD) to provide detailed insight into the blood flow surrounding theaortic valve. These insights have then been used to facilitate thedesign and construction of heart valves with desirable hemodynamicproperties that maximize functionality and durability while minimizingthe potentially fatal risks of valvular malfunction and adversephysiological response.

In recent years, hemodynamic modeling of heart valves has included bothsurgically implanted and transcatheter prostheses, but the focus of moststudies remains the aortic valve. With the rapidly expanding clinicaldeployment of transcatheter aortic heart valves, modeling and simulationresults have helped understand and characterize the unique hemodynamicchallenges of transcatheter designs compared to traditional surgicalimplantation of aortic valves. In particular, computer modeling may beused to quantify downstream flow effects in the aortic arch and leafletstresses, which impact device efficacy, robustness, durability, andlongevity.

To date, all computer modeling and simulation studies of heart valveshave been focused on evaluating and improving prosthetic valve designand function.

BRIEF SUMMARY OF THE PRESENT INVENTION

The machine learning system and method described in this disclosurefacilitates the diagnosis and treatment of heart valve disease anddiseases of the surrounding inflow/outflow tracts. Further, the systemand method facilitate the evaluation and assessment of valvular repairand/or prosthetic performance in patients who have undergone heart valvetreatment. In addition to using routine physiological and geometric datagathered through two- and/or three-dimensional imaging studies, themachine learning system may also incorporate hemodynamic data into theconstruction and utilization of an accurate geometric and functionalunderstanding from which to assess valvular condition and function.

In one aspect, a machine learning system for evaluating at least onecharacteristic of a heart valve, an inflow tract and/or an outflow tractmay include a training mode and a production mode. The training mode maybe configured to train a computer and construct a transformationfunction to predict an unknown anatomical characteristic and/or anunknown physiological characteristic of a heart valve, an inflow tractand/or an outflow tract, using a known anatomical characteristic and/ora known physiological characteristic of the heart valve, inflow tractand/or outflow tract. The production mode may be configured to use thetransformation function to predict the unknown anatomical characteristicand/or the unknown physiological characteristic of the heart valve,inflow tract and/or outflow tract, based the known anatomicalcharacteristic and/or the known physiological characteristic of theheart valve, inflow tract and/or outflow tract.

In some embodiments, the training mode is configured to compute andstore in a feature vector the known anatomical characteristic and/orknown physiological characteristic of the heart valve, inflow tractand/or outflow tract. In some embodiments, the training mode isconfigured to calculate an approximate blood flow through the heartvalve, inflow tract and/or outflow tract. In some embodiments, thetraining mode is further configured to store quantities associated withthe approximate blood flow through the heart valve, inflow tract and/oroutflow tract. Optionally, the training mode may be further configuredto perturb the at least one known anatomical characteristic or knownphysiological characteristic of the heart valve, inflow tract and/oroutflow tract stored in the feature vector. In some embodiments, thetraining mode may be further configured to calculate a new approximateblood flow through the heart valve, inflow tract and/or outflow tractwith the perturbed known anatomical characteristic and/or knownphysiological characteristic. In some embodiments, the training mode maybe further configured to store quantities associated with the newapproximate blood flow through the perturbed heart valve, inflow tractand/or outflow tract. In some embodiments, the training mode may befurther configured to repeat the perturbing, calculating and storingsteps to create a set of feature vectors and quantity vectors and togenerate the transformation function.

In one embodiment, the training mode may be further configured toperform a method, involving: receiving patient-specific data includinganatomic data, physiologic data and/or hemodynamic data; generating adigital model of the at least one heart valve, inflow tract or outflowtract, based on the received data; discretizing the digital model;applying boundary conditions to at least one inflow portion and at leastone outflow portion of the digital model; and initializing and solvingmathematical equations of blood flow through the digital model. In someembodiments, the method may further involve storing quantities andparameters that characterize an anatomic state and/or a physiologicstate of the digital model and the blood flow. In some embodiments, themethod may further involve perturbing an anatomic parameter and/or aphysiologic parameter that characterizes the digital model. In anotherembodiment, the method may further involve re-discretizing and/orre-solving the mathematical equations with the anatomic parameter and/orphysiologic parameter. In another embodiment, the method may furtherinvolve storing quantities and parameters that characterize the anatomicstate and/or the physiologic state of the perturbed model and bloodflow.

In some embodiments, the production mode may be configured to receiveone or more feature vectors. In some embodiments, the production modemay be configured to apply the transformation function to the featurevectors. In some embodiments, the production mode may be configured togenerate one or more quantities of interest. In some embodiment, theproduction mode may be configured to store the quantities of interest.In some embodiments, the production mode may be configured to processthe quantities of interest to provide data for use in at least one ofevaluation, diagnosis, prognosis, treatment or treatment planningrelated to a heart in which the heart valve resides.

In another aspect, a computer-implemented machine learning method forevaluating at least one characteristic of a heart valve, an inflowtract, and/or an outflow tract may involve training a computer by usinga training mode of a machine learning system to construct atransformation function to predict an unknown anatomical characteristicand/or an unknown physiological characteristic a heart valve, an inflowtract and/or an outflow tract, using a known anatomical characteristicand/or a known physiological characteristic of the heart valve, inflowtract and/or outflow tract. The method may also involve using aproduction mode of the machine learning system to direct thetransformation function to predict the unknown anatomical characteristicand/or the unknown physiological characteristic of the heart valve,inflow tract and/or outflow tract, based on the known anatomicalcharacteristic and/or the known physiological characteristic of theheart valve, inflow tract and/or outflow tract.

In some embodiments, the method may further involve using the trainingmode to compute and store in a feature vector the known anatomicalcharacteristic and/or known physiological characteristic of the heartvalve, inflow tract and/or outflow tract. In some embodiments, themethod may further involve using the training mode to calculate anapproximate blood flow through the heart valve, inflow tract and/oroutflow tract. In some embodiments, the method may further involve usingthe training mode to store quantities associated with the approximateblood flow through the heart valve, inflow tract and/or outflow tract.In some embodiments, the method may further involve using the trainingmode to perturb the known anatomical characteristic and/or knownphysiological characteristic of the heart valve, inflow tract and/oroutflow tract stored in the feature vector. In some embodiments, themethod may further involve using the training mode to calculate a newapproximate blood flow through the heart valve, inflow tract and/oroutflow tract with the perturbed known anatomical characteristic and/orknown physiological characteristic. In some embodiments, the method mayfurther involve using the training mode to store quantities associatedwith the new approximate blood flow through the perturbed heart valve,inflow tract and/or outflow tract. In some embodiments, the method mayfurther involve using the training mode to repeat the perturbing,calculating and storing steps to create a set of feature vectors andquantity vectors and to generate the transformation function.

In some embodiments, the method may further involve using the trainingmode to perform the following steps: receiving patient-specific dataselected from the group consisting of anatomic data, physiologic data,and hemodynamic data; generating a digital model of the at least oneheart valve, inflow tract or outflow tract, based on the received data;discretizing the digital model; applying boundary conditions to at leastone inflow portion and at least one outflow portion of the digitalmodel; and initializing and solving mathematical equations of blood flowthrough the digital model. In some embodiments, the method may furtherinvolve storing quantities and parameters that characterize an anatomicstate and/or a physiologic state of the digital model and the bloodflow. In some embodiments, the method may further involve perturbing ananatomic parameter and/or a physiologic parameter that characterizes thedigital model. In some embodiments, the method may further involvere-discretizing or re-solving the mathematical equations with the atleast one anatomic parameter or physiologic parameter. In someembodiments, the method may further involve storing quantities andparameters that characterize the anatomic state and/or the physiologicstate of the perturbed model and blood flow.

In some embodiments, the method may further involve receiving one ormore feature vectors with the production mode. In some embodiments, themethod may further involve using the production mode to apply thetransformation function to the feature vectors. In some embodiments, themethod may further involve using the production mode to generate one ormore quantities of interest. In some embodiments, the method may furtherinvolve using the production mode to store the quantities of interest.In some embodiments, the method may further involve using the productionmode to process the quantities of interest to provide data for use inevaluation, diagnosis, prognosis, treatment and/or treatment planningrelated to a heart in which the heart valve, inflow tract and/or outflowtract resides.

In another aspect, a non-transitory computer readable medium for use ona computer system may contain computer-executable programminginstructions for performing a method for evaluating at least onecharacteristic of a heart valve, an inflow tract, an outflow tract or acombination thereof The method may include any of the features and/oraspects described above.

In various other aspects, this disclosure describes various methodembodiments. Examples of such method embodiments include: A method ofusing data analysis and/or machine learning to construct atransformation function to compute the anatomic and/or physiologic stateof at least one heart valve and/or the corresponding inflow/outflowtracts; A method of using computer modeling and simulation and/orclinical data to generate a set of feature vectors that are used asinput into a machine learning algorithm; A method of using machinelearning to assess anatomy and/or physiology of at least one heart valveand/or the corresponding inflow/outflow tracts, comprising usingpatient-specific data derived from one or more interventional ornon-interventional methods and/or results generated by computer modelingand simulation; A method of using machine learning to assess the anatomyand/or physiology of at least one heart valve and/or the correspondinginflow/outflow tracts, comprising using patient-specific data derivedfrom one or more interventional or non-interventional methods to performsensitivity and uncertainly analyses; A method of using machine learningto assess the anatomy and/or physiology of at least one heart valveand/or the corresponding inflow/outflow tracts, comprising usingpatient-specific data derived from one or more interventional ornon-interventional methods to aid in the diagnosis, assessment and/orprognosis of a diseased state; and A method of using machine learning toassess the anatomy and/or physiology of at least one heart valve and/orthe corresponding inflow/outflow tracts, comprising usingpatient-specific data derived from one or more interventional ornon-interventional methods to aid in the planning of prosthetic heartvalve implantation.

These and other aspects and embodiments will be described in furtherdetail below, in reference to the attached drawing figures.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a machine learning system, according to oneembodiment;

FIG. 2 is a flow diagram outlining a modeling and simulation method fora training portion of a machine learning system, according to oneembodiment;

FIG. 3 is a flow diagram outlining execution of a training portion of amachine learning system, according to one embodiment;

FIG. 4 is a flow diagram outlining execution of a production portion ofa machine learning system, according to one embodiment;

FIG. 5 is a perspective view of a simplified geometric model, based onpatient-specific anatomic parameters, of an aortic valve and surroundingcardiac inflow and outflow vessels, according to one embodiment;

FIG. 6 is a perspective view of a simplified geometric model with thecomputational surface mesh, based on patient-specific anatomicparameters, of the aortic valve and the surrounding cardiac inflow andoutflow vessels, according to one embodiment; and

FIGS. 7A-7D are perspective views of various representative polyhedraused to discretize the interior volume of the geometric model, accordingto various embodiments.

DETAILED DESCRIPTION

This disclosure describes machine learning systems and methods thatqualitatively and quantitatively characterize anatomic geometry and/orphysiology of a heart valve, one or more inflow tracts of a heart valve,and/or one or more outflow tracts of a heart valve. Throughout thisdisclosure, reference may be made to characterizing or evaluating aheart valve. In all embodiments, such characterization, evaluation, etc.may be performed on a heart valve, one or more inflow tracts of a heartvalve, and/or one or more outflow tracts of a heart valve. For enhancedreadability of the description, however, the phrase “heart valve” maysimply be used, rather than repeating “a heart valve, one or more inflowtracts of a heart valve, and/or one or more outflow tracts of a heartvalve” in each instance. Any embodiment described for use in evaluatinga heart valve may additionally or alternatively be used to evaluate oneor more inflow tracts of a heart valve and/or one or more outflow tractsof a heart valve. The various embodiments described herein may beapplied to any single heart valve, a combination of multiple heartvalves, and/or combinations of one or more heart valves and one or morecoronary blood vessels. Although occasional references may be made toone specific heart valve, inflow tract, or outflow tract, these specificreferences should not be interpreted as limiting the scope of thisdisclosure. For example, the aortic heart valve is used throughout thisdisclosure as a specific example of a prototypical heart valve.Illustration of the systems and methods via the example of the aorticheart valve, however, is not intended to limit the scope of the computermodeling and simulation systems and methods disclosed herein.

Referring to FIG. 1 and according to one embodiment, a machine learningsystem 30 may include two modes: a training mode 32 and a productionmode 34. The two modes 32, 34 may be embodied in a computer systemand/or a computer readable medium. The system 30 may execute the twomodes in series, where the training mode 32 is executed first, and theproduction mode 34 is executed second. The training mode 32 may beconfigured to develop analytical capabilities in a computer system thatenable the computer system to predict unknown anatomic and/orphysiologic characteristics of one or more heart valves and/or thesurrounding inflow/outflow tracts. These predictive capabilities may bedeveloped by the analysis and/or evaluation of known anatomic and/orphysiologic characteristics of one or more heart valves and/or thesurrounding inflow/outflow tracts. Using a collection of known anatomicand/or physiologic characteristics, a computer may be “trained” topredict various unknown anatomic and/or physiologic characteristics. Theabstract mapping that transforms a set of known characteristics into oneor more predictions of unknown characteristics may be referred to as the“transformation function.” In some embodiments, the training mode 32 maybe configured to construct the transformation function.

The production mode 34 of the machine learning system 30 may use thetransformation function to predict anatomic and/or physiologiccharacteristics that are unknown from a collection of anatomic and/orphysiologic characteristics that are known. Hence, during execution ofthe production mode 34, input into the transformation function may be aset of known anatomic and/or physiologic characteristics (e.g., the sameanatomic and/or physiologic characteristics used during the trainingmode 32). The output of the transformation function may be one or moreanatomic and/or physiologic characteristics that were previouslyunknown.

The training mode 32 and production mode 34 may be implemented in anumber of different ways in various alternative embodiments. Oneembodiment of a method for implementing the training mode 32 andproduction mode 34 of a machine learning system is described in moredetail immediately below. This is only one exemplary embodiment,however, and should not be interpreted as limiting the scope of themachine learning system 30 as described above.

Training Mode:

During the training mode 32 of the machine learning system 30, anatomicand/or physiologic data may be acquired that characterize the state andoperation of a heart valve and its corresponding inflow/outflow tracts.These data may be collected through one or more acquisition methods,including, for example, analysis of radiological images, analysis ofechocardiographic images, Doppler and/or electrophysiologic signals,clinical instruments (e.g., blood pressure gauge, stethoscope), andcomputer modeling/simulation. Referring to the aortic valve as anexample, anatomic and/or physiologic characterization parameters mayinclude, for example:

-   -   flow characteristics (e.g., velocities, velocity gradients,        pressures, pressure gradients, turbulence intensity, shear        stress) at single or multiple location(s) within the left        ventricular outflow tract (LVOT), valsalva sinuses (VS),        sinotubular junction (SJ), ascending aorta (AA) or vasculature        surrounding one or more heart valve(s);    -   approximations to flow, flow properties or flow characteristics        via simplified and/or analytical models (e.g., pipe flow,        orifice flow);    -   size and/or shape characteristics at single or multiple        location(s) within the LVOT, VS, SJ, AA, or surrounding        vasculature, e.g., diameter, eccentricity, cross-sectional area,        axial length, length of major axis, length of minor axis,        geometric gradient(s);    -   height, shape, lateral profile, thickness, degree of        calcification, location of calcification, angular size, angular        separation, radial length, tip sharpness, rigidity, flexibility,        movement, tissue properties, overlap, and/or attachment angle(s)        of one or more valve leaflets;    -   location, attachment angles, and/or sizes of one or more        coronary arteries;    -   geometric orifice area and/or estimated orifice area of the        valve;    -   size, shape, location, density, composition, and/or extent of        vascular calcification;    -   stroke volume and/or cardiac output;    -   blood pressure, heart rate, and/or hematocrit of the patient;        and    -   age, height, weight, body mass index, race, and/or gender of the        patient.

Referring to FIG. 2, one embodiment of a method for implementing thetraining mode 32 of the machine learning system 30 is illustrated. Inthis embodiment, the training mode 32 of the machine learning system 30is coupled with a modeling and simulation system (not shown), which mayprovide input data for the machine learning system 30. Hence, themodeling and simulation system may operate in conjunction with themachine learning system 30, in that it may provide anatomic and/orphysiologic data to the machine learning system 30. These data may serveas the foundation from which the machine learning system 30 learns toperform the desired task(s).

A first step of the embodiment described in FIG. 2 may involve importingpatient-specific geometric, anatomic, physiologic, and/or hemodynamicdata into the computer system 100. A second step may involveconstructing a (possibly parameterized) geometric model using theimported data 200. One embodiment of a geometric model 10 is illustratedin FIG. 5.

As illustrated in FIG. 5, in one embodiment, the geometric model 10 maybe a multi-dimensional digital representation of the relevant patientanatomy, which may include at least one heart valve 12 (the aortic valvein one embodiment), at least a portion of an inflow vessel 14 (or“inflow tract”), and at least a portion of an outflow vessel 16 (or“outflow tract”) of the valve 12. The model may also include one or moreventricles and/or atria of the heart or a portion thereof and/or one ormore coronary vessels or a portion thereof. The geometric model iscreated from patient-specific anatomical, geometric, physiologic, and/orhemodynamic data. In some embodiments, the model may be created usingexclusively imaging data. Alternatively, the model may be created usingimaging data and at least one clinically measured flow parameter.Imaging data may be obtained from any suitable diagnostic imagingexam(s), such as those listed above. Clinically measured flow parametersmay be obtained from any suitable test(s), such as those listed above.

The model 10 may also contain at least one inflow boundary and at leastone outflow boundary, through which blood flows in and out of themulti-dimensional model 10, respectively. These inflow and outflowboundaries denote finite truncations of the digital model 10 and are notphysically present in a patient. The digital geometric model 10 may becreated using methods of applied mathematics and image analysis, such asbut not limited to image segmentation, machine learning, computer aideddesign, parametric curve fitting, and polynomial approximation. In someembodiments, a hybrid approach, which combines a collection of geometricmodeling techniques, may also be utilized. The final, multi-dimensionalmodel 10 provides a digital surrogate that captures the relevantphysical features of the anatomic topology under consideration and maycontain one or more morphological simplifications (e.g., symmetry,smoothing) that exploit the underlying geometric features of thepatient-specific valvular and vascular system being considered.

Referring again to FIG. 1, following the construction of the digitalmodel 200, the modeling and simulation portion of the machine learningsystem may discretize the surface and volume of the model into a finitenumber of partitions 300. These individual and non-overlappingpartitions, called “elements,” may facilitate the application andsolution of the physical laws of motion that govern blood flow throughthe geometric model. The set of surface and volume elements used todiscretize the model, collectively referred to as the “mesh,” transformthe continuous geometric model into a set of mesh points and edges,where each element point in the mesh has discrete x, y, and z spatialcoordinates, and each element edge is bounded by two mesh points and hasa finite length.

An illustration of a representative mesh 21 that discretizes the surfaceof a geometric model 20 is shown in FIG. 6. The geometric model 20, inthis embodiment, includes an aortic valve 22, inflow tract 24 andoutflow tract 26. This illustration of the model 20 is used to show themesh 21 and is intended for exemplary purposes only.

The shape of the surface elements created by the modeling and simulationportion of the machine learning system may take the form of any closedpolygon, but the surface mesh typically contains a collection oftriangles, convex quadrilaterals or a combination thereof. Referring toFIGS. 7A-7D, volume elements may be created by the modeling andsimulation system and are used to fill the interior of the modelcompletely. Each volume element may take the form of any closedpolyhedron, but the volume mesh (i.e., the set of volume elements)typically contains a collection of tetrahedra (FIG. 7A), hexahedra (FIG.7B), pyramids (FIG. 7C), wedges (FIG. 7D), or a combination thereof. Thesurface and volume mesh densities, which determine the spatialresolution of the discrete model, may vary in space and time. The localdensities of the surface and volume meshes may depend on the complexityof the local topology of the underlying geometric model: more complexlocal topology may require higher spatial resolution, and therefore ahigher mesh density, to resolve than local regions of less complextopology.

The modeling and simulation portion of the machine learning method mayuse CFD to simulate blood flow through the discretized geometric model.Blood may be represented as a Newtonian or non-Newtonian fluid, andblood flow may be represented physically by the conservation of mass,momentum, and energy (or a combination thereof) and mathematically bythe fluid flow equations (e.g., continuity, Navier-Stokes equations)with appropriate initial and boundary conditions. The boundaryconditions may be a function of time and/or space. Initial and boundaryconditions may be determined from empirical or heuristic relationships,clinical data, mathematical formulas or a combination thereof, and themodel boundaries may be rigid or compliant or a combination thereof. Themathematical equations and corresponding initial and boundary conditionsmay be solved using conventional mathematical techniques, which includeanalytical or special functions, numerical methods (e.g., finitedifferences, finite volumes, finite elements, spectral methods), methodsof machine learning or a hybrid approach that combines various aspectsof the methods listed.

As a next step in the modeling and simulation portion of the machinelearning method, and referring again to FIG. 2, boundary conditions maybe applied to a discrete patient model 400. The boundary flow conditionsmay be obtained from patient-specific clinical measurements (e.g., pulsewave Doppler echocardiography, continuous wave Doppler echocardiography,MRI), in which case they may be prescribed to the model in a manner thatis consistent with clinical observations and measurements. In addition,inflow and outflow boundary conditions may be prescribed to compensatefor underlying psychological or medical conditions such as pain,anxiety, fear, anemia, hyperthyroidism, left ventricular systolicdysfunction, left ventricular hypertrophy, hypertension orarterial-venous fistula, which may produce clinically misleading resultsupon which medical evaluations, diagnostics, treatment planning ortreatment(s) may be based.

With continued reference to FIG. 2, following the initialization of theblood flow equations, the equations are solved, and hemodynamicquantities of interest are computed 500 by the modeling and simulationsystem, which may be a component of the training mode 32 of the machinelearning system 30. The hemodynamic quantities of interest computed bythe modeling and simulation system may include, for example, the flowvelocity at one or more points in the computational domain, velocitygradients, pressure, pressure gradients, shear stress, the wall shearstress at location(s) on the heart valve, etc.

Following the solution of the mathematical equations and computation ofthe quantities of interest, the anatomic and physiologic parameters thatare inputs into the modeling and simulation system, collectivelyreferred to as “features,” may be assembled into a vector 600. Thisvector of anatomic and physiologic features is referred to as a “featurevector.” As an illustrative example, numerical quantities contained in afeature vector may include some or all of the parameters (or features)outlined above, e.g., LVOT diameter, LVOT velocity, LVOT cross sectionalarea, height of each valvular leaflet, thickness of each valvularleaflet, diameter of the ascending aorta, etc. The correspondinghemodynamic quantities of interest, which may be computed from the CFDsimulation from an anatomic model that may be characterized by featuresin the feature vector, may also assembled into a vector, which may bereferred to as the “quantity of interest vector.” The quantity ofinterest vector may include, for example, wall shear stress, pressure,pressure gradients, velocity, velocity gradients, and/or shear atvarious locations throughout the model, etc. Both the feature andquantity of interest vectors may then be saved for use during othersteps of the machine learning process. Note that a feature vector andthe corresponding quantity of interest vector may have differentlengths. In addition, entries within the feature and quantity ofinterest vector may be obtained from different mechanisms (e.g.,clinical data, numerical simulations, estimated approximation).Nonetheless, each feature vector is associated with a quantity ofinterest vector and vice versa.

Referring to FIG. 2, a next step in the method may involve modifying (or“perturbing”) the digital model and/or flow condition to representperturbed anatomic and/or physiologic conditions 700. As an example ofan anatomic perturbation, one valve leaflet may be retracted to increasethe geometric orifice area of the valve. As an example of a physiologicperturbation, the inflow velocity through the LVOT may be increased ordecreased.

As illustrated in FIG. 2, following modification(s) to the anatomicand/or physiologic conditions 700, steps 300-700 of the modeling andsimulation portion of the machine learning system may be repeated 800,until a desired number of feature vectors and the correspondingquantities of interest vectors are obtained. Note that each iteration ofsteps 300-700 produces a new feature vector and a new quantity ofinterest vector. Though one or more entries within the feature and/orquantity of interest vector may change with each iteration of steps300-700, the representation and length of each vector remains the same.That is, each digital model is represented by the same characteristicsand the same number of characteristics, and this collection ofcharacteristics is contained within the feature vector. Further, thecorresponding quantities of interest for each digital model are thesame. The sets of feature and quantity of interest vectors may then bestored on digital media.

In some embodiments, and referring now to FIG. 3, a machine learningmethod may involve applying machine learning algorithms to a collectionof feature and quantity of interest vectors from the method describedabove and illustrated in FIG. 2. The collection of feature and quantityof interest vectors may first be imported into machine learning software900. The machine learning software may then apply one or more analysisor machine learning algorithms (e.g., decision trees, support vectormachines, regression, Bayesian networks, random forests) to the set offeature and quantity of interest vectors 1000. Following the applicationof machine learning algorithm(s), a transformation function isconstructed 1100. This transformation function may serve as a mappingbetween the one or more features contained within a feature vector andthe one or more quantities of interest computed from the modeling andsimulation portion of the machine learning system. Hence, the input intothe transformation function is a feature vector, and the output of thetransformation function is a quantity of interest vector. To test theaccuracy of the transformation function created by the machine learningalgorithm, for example, one of the feature vectors used to create thetransformation function may be used as input into the transformationfunction. The expected output from the transformation function is thecorresponding quantity of interest vector, though the quantity ofinterest output vector may not be reproduced exactly by thetransformation function. The transformation function may be stored ondigital media for use, for example, during the production mode of themachine learning system 1200.

Following construction of the transformation function by the analysisand machine learning algorithm(s), functioning of the training mode 32of the machine learning system 30, as described in the presentembodiment, may be complete. Subsequently, the transformation functionmay be used in the production mode 34 of the machine learning system 30.

Production Mode:

The production mode 34 of the machine learning system 30 may be usedafter the training mode 32. The production mode 34 may be configured tocompute quantity of interest vectors rapidly and accurately by applyingthe transformation function to a variety of feature vectors. In some butnot all cases, these feature vectors might have been used to constructthe transformation function.

Referring now to FIG. 4, in one embodiment, the production mode 34 ofthe machine learning system 30 may first be used to import thetransformation function and one or more feature vectors 1300, whichcontain the same set of features used during the training mode 32. Thefeature vectors used during the production mode 34 may or may not havebeen used during the training mode to construct the transformationfunction, and therefore the transformation function may not have beenconstructed with data contained within these feature vectors. The numberof features within each feature vector and the quantities represented byeach feature within each feature vector, however, are the same as thoseused to construct the transformation function.

The transformation function may then be applied to the one or morefeature vectors 1400. Hence, the inputs to the transformation functionduring the production mode 34 of the machine learning system 30 may beone or more feature vectors, and the output from the transformationfunction may be a vector that contains the quantities of interest. Thequantity of interest vector outputted from the transformation functionmay then be stored 1500, e.g., on digital media.

The quantities of interest contained within the quantity of interestvector may include qualitative and/or quantitative geometric andhemodynamic information. These data may be further analyzed and assessedthrough various mechanisms of post-processing to reveal patient-specificanatomic and/or physiologic and/or hemodynamic information that may aidin the diagnosis, treatment, and/or treatment planning of a patient.These qualitative and quantitative data may also be used to guideclinical decision-making and/or provide predictive information aboutdisease progression or risk stratification.

Quantities of interest and/or data derived from the machine learningsystem 30 may be delivered to physicians, who may use these data forclinical decision-making Delivery of patient-specific information tophysicians may occur via integrated or stand-alone software systems,numerical data, graphs, charts, plots, verbal discussions, writtencorrespondence, electronic media, etc. or a combination thereof. Thesedata may then be used by an individual physician or by a team ofphysicians to develop a complete, comprehensive, and accurateunderstanding of patient cardiac health and to determine whether or notmedical treatment is warranted. If medical treatment is warranted,results from the machine learning system 30 may be used to guideclinical decision-making By way of example, specific ways in whichoutput from the machine learning system 30 may be incorporated into theclinical management of cardiac patients include:

-   -   analysis of heart valve operation, including diagnosing the        severity, functional significance, and clinical response to        abnormal heart valve operation;    -   patient-specific selection, sizing, and positioning of        prosthetic heart valves, including surgical, transcatheter, and        valve-in-valve treatments; and    -   patient monitoring and/or follow-up.

The list of applications outlined above is for example purposes only,and the list is not intended to be exhaustive.

The machine learning system 30 may provide a fast and accurate virtualframework for conducting patient-specific sensitivity analyses. Suchanalyses may assess the relative impacts of geometric and/or hemodynamicchanges to the anatomic, physiologic, and/or hemodynamic state of apatient; these state changes may then be assessed for functional andclinical significance thereby estimating patient response to therapy (orlack thereof), disease progression, and/or patient-specific riskstratification. Sensitivity analyses may be performed, for example, byapplying the transformation function, which is computed during thetraining mode 32 of the machine learning system 30, to multiple featurevectors that describe variations of specific anatomic and/or physiologicfeatures of the patient. Although construction of the transformationfunction during the training mode 32 is likely best to include featurevectors that are similar to those used during a sensitivity analysis, itis important to note that the transformation function may not requirere-computation during a sensitivity analysis study. Hence, the machinelearning system 30 may enable a rapid evaluation of numerous anatomic,physiologic, and/or hemodynamic scenarios that run in a virtualenvironment without exposing patients to any medical risks. Results fromthe plethora of transformation function evaluations conducted during asensitivity analysis may be aggregated and presented to physicians forclinical decision-making Further, results from sensitivity analyses mayalso be used in conjunction with uncertainty analyses to, for example,assess global and/or local uncertainties of anatomic, physiologic,and/or hemodynamic results produced by the machine learning system 30.

The machine learning system 30 enables planning of heart valvereplacement therapy and the selection of optimal valve deployment. Forexample, executing the machine learning system 30 described hereinprovides an accurate assessment of anatomic, physiologic, and/orhemodynamic consideration for valvular deployment and function, e.g.,size, deployment mechanism, deployment angle. Hence, the machinelearning system 30 and methods for using it provide a complete frameworkthat enables the accurate assessment of anatomic structure in relationto native and prosthetic heart valves and their correspondinginflow/outflow tracts. This information may be used by physicians tomake clinical decisions regarding patient treatment of heart valvedisease as to maximize the benefits to each patient.

Although the above description highlights a number of embodiments andexamples, the present invention extends beyond the specificallydisclosed embodiments to other alternative embodiments and/or uses ofthe invention and modifications and equivalents thereof. Thus, the scopeof the present invention should not be limited by the particulardisclosed embodiments described above, but should be determined only bya fair reading of the claims that follow.

1. A machine learning system for evaluating at least one characteristicof a heart valve, an inflow tract, an outflow tract or a combinationthereof, the system comprising: a training mode configured to train acomputer and construct a transformation function to predict at least oneof an unknown anatomical characteristic or an unknown physiologicalcharacteristic of at least one of a heart valve, an inflow tract or anoutflow tract, using at least one of a known anatomical characteristicor a known physiological characteristic of the at least one heart valve,inflow tract or outflow tract; and a production mode configured to usethe transformation function to predict at least one of the unknownanatomical characteristic or the unknown physiological characteristic ofthe at least one heart valve, inflow tract or outflow tract, based on atleast one of the known anatomical characteristic or the knownphysiological characteristic of the at least one heart valve, inflowtract or outflow tract, wherein the production mode is furtherconfigured to receive one or more feature vectors.
 2. A system as inclaim 1, wherein the training mode is configured to compute and store ina feature vector the at least one known anatomical characteristic orknown physiological characteristic of the at least one heart valve,inflow tract or outflow tract.
 3. A system as in claim 2, wherein thetraining mode is configured to calculate an approximate blood flowthrough the at least one heart valve, inflow tract or outflow tract. 4.A system as in claim 3, wherein the training mode is further configuredto store quantities associated with the approximate blood flow throughthe at least one heart valve, inflow tract or outflow tract.
 5. A systemas in claim 4, wherein the training mode is further configured toperturb the at least one known anatomical characteristic or knownphysiological characteristic of the at least one heart valve, inflowtract or outflow tract stored in the feature vector.
 6. A system as inclaim 5, wherein the training mode is further configured to calculate anew approximate blood flow through the at least one heart valve, inflowtract or outflow tract with the perturbed at least one known anatomicalcharacteristic or known physiological characteristic.
 7. A system as inclaim 6, wherein the training mode is further configured to storequantities associated with the new approximate blood flow through theperturbed at least one heart valve, inflow tract or outflow tract.
 8. Asystem as in claim 7, wherein the training mode is further configured torepeat the perturbing, calculating and storing steps to create a set offeature vectors and quantity vectors and to generate the transformationfunction.
 9. (canceled)
 10. A system as in claim 1, wherein theproduction mode is configured to apply the transformation function tothe feature vectors.
 11. A system as in claim 10, wherein the productionmode is configured to generate one or more quantities of interest.
 12. Asystem as in claim 11, wherein the production mode is configured tostore the quantities of interest.
 13. A system as in claim 12, whereinthe production mode is configured to process the quantities of interestto provide data for use in at least one of evaluation, diagnosis,prognosis, treatment or treatment planning related to a heart in whichthe heart valve resides.
 14. A computer-implemented machine learningmethod for evaluating at least one characteristic of a heart valve, aninflow tract, an outflow tract or a combination thereof the methodcomprising: training a computer by using a training mode of a machinelearning system to construct a transformation function to predict atleast one of an unknown anatomical characteristic or an unknownphysiological characteristic of at least one of a heart valve, an inflowtract or an outflow tract, using at least one of a known anatomicalcharacteristic or a known physiological characteristic of the at leastone heart valve, inflow tract or outflow tract; using the training modeto compute and store in a feature vector the at least one knownanatomical characteristic or known physiological characteristic of theat least one heart valve, inflow tract or outflow tract; and using aproduction mode of the machine learning system to direct thetransformation function to predict at least one of the unknownanatomical characteristic or the unknown physiological characteristic ofthe at least one heart valve, inflow tract or outflow tract, based on atleast one of the known anatomical characteristic or the knownphysiological characteristic of the at least one heart valve, inflowtract or outflow tract.
 15. (canceled)
 16. A method as in claim 14,further comprising using the training mode to calculate an approximateblood flow through the at least one heart valve, inflow tract or outflowtract.
 17. A method as in claim 16, further comprising using thetraining mode to store quantities associated with the approximate bloodflow through the at least one heart valve, inflow tract or outflowtract.
 18. A method as in claim 17, further comprising using thetraining mode to perturb the at least one known anatomicalcharacteristic or known physiological characteristic of the at least oneheart valve, inflow tract or outflow tract stored in the feature vector.19. A method as in claim 18, further comprising using the training modeto calculate a new approximate blood flow through the at least one heartvalve, inflow tract or outflow tract with the perturbed at least oneknown anatomical characteristic or known physiological characteristic.20. A method as in claim 19, further comprising using the training modeto store quantities associated with the new approximate blood flowthrough the perturbed at least one heart valve, inflow tract or outflowtract.
 21. A method as in claim 20, further comprising using thetraining mode to repeat the perturbing, calculating and storing steps tocreate a set of feature vectors and quantity vectors and to generate thetransformation function.
 22. A method as in claim 14, further comprisingusing the training mode to perform the following steps: receivingpatient-specific data selected from the group consisting of anatomicdata, physiologic data, and hemodynamic data; generating a digital modelof the at least one heart valve, inflow tract or outflow tract, based onthe received data; discretizing the digital model; applying boundaryconditions to at least one inflow portion and at least one outflowportion of the digital model; and initializing and solving mathematicalequations of blood flow through the digital model.
 23. A method as inclaim 22, further comprising storing quantities and parameters thatcharacterize at least one of an anatomic state or a physiologic state ofthe digital model and the blood flow.
 24. A method as in claim 23,further comprising perturbing at least one of an anatomic parameter or aphysiologic parameter that characterizes the digital model.
 25. A methodas in claim 24, further comprising at least one of re-discretizing orre-solving the mathematical equations with the at least one anatomicparameter or physiologic parameter.
 26. A method as in claim 25, furthercomprising storing quantities and parameters that characterize at leastone of the anatomic state or the physiologic state of the perturbedmodel and blood flow.
 27. A method as in claim 19, further comprisingreceiving one or more feature vectors with the production mode.
 28. Amethod as in claim 27, further comprising using the production mode toapply the transformation function to the feature vectors.
 29. A methodas in claim 28, further comprising using the production mode to generateone or more quantities of interest.
 30. A method as in claim 29, furthercomprising using the production mode to process the quantities ofinterest to provide data for use in at least one of evaluation,diagnosis, prognosis, treatment or treatment planning related to a heartin which the at least one heart valve, inflow tract or outflow tractresides.